博碩士論文 983202074 詳細資訊




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姓名 陳世倫(Shih-lun Chen)  查詢紙本館藏   畢業系所 土木工程學系
論文名稱 利用基因規劃法進行車輛偵測器資料填補
(Imputing Vehicle Detector Data by Genetic Programming)
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摘要(中) 本研究主要目的為針對雪山隧道路段車輛偵測器之遺漏值,利用基因規劃法進行實證分析,以求得最佳填補函數。首先,進行單一屬性資料填補,採用上、下游累積偵測器資料並逐步向外對稱延伸累積,分別進行流量、速率、佔有率填補,分析填補績效與投入填補之車輛偵測器數量是否相關。接著以多屬性資料分別進行流量、速率、佔有率資料填補,以求得最佳之填補函數。之後,再將單一屬性與多屬性資料分別填補流量、速率、佔有率之績效進行排名,以求得進行填補時採用之優先順序。最後,將基因規劃法之填補績效與回饋式類神經網路進行績效比較。
結果顯示,利用基因規劃法之填補績效優於回饋式類神經網路之填補績效。綜合流量、速率與佔有率之填補績效,流量&速率填補排名第一,速率&流量&佔有率填補排名第二,而速率填補績效最差,排名第七。以填補流量績效而言,利用流量資料取上下游累積至第5組偵測器進行填補可獲得最佳填補績效,MAPE值為5.23%;以填補速率績效而言,利用流量&速率資料取上下游累積至第3組偵測器進行填補可獲得最佳填補績效,MAPE值為0.96%;以填補佔有率績效而言,利用佔有率資料,取上下游累積至第11組偵測器進行填補可獲得最佳填補績效,MAPE值為10.67%。
摘要(英) To search for the optimal imputation of Vehicle Detector, this paper, we carried out an empirical analysis for missing value of Hshehshan tunnel via Genetic Programming. We, at first, use signal attribute data impute missing value by accumulated nearest pairs of up- and downstream vehicle detectors, and analyze the relation between performance and number of vehicle detectors, whereupon, we imputed missing value by multi-attribute data. After testing data imputation, we ranked all types of imputation according to the performance. Finally, Recurrent Neural Network was selected to compare with Genetic Programming.
The results showed that the performance of Genetic programming is better than Recurrent Neural Network. If we ranked all types of imputation according to conbined the three imputation performance, the rank as follows, flow&speed imputaiotn is 1st, speed&flow&occ imputation is 2st and speed imputation is the worst. For flow imputation, we use flow data, the accumulated nearest five pairs of detector up- and downstream could be input for the highest accuracy. For speed imputation, we use flow&speed data, the accumulated nearest three pairs of detector up- and downstream could be input for the highest accuracy. For occupancy imputation, we use occupancy data, the accumulated nearest eleven pairs of detector up- and downstream could be input for the highest accuracy.
關鍵字(中) ★ 遺漏值
★ 基因規劃法
★ 填補績效
關鍵字(英) ★ Missing Value
★ Imputation Performance
★ Genetic Programming
論文目次 摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vi
第一章 緒論 1
1.1研究背景與動機 1
1.2研究目的 2
1.3研究範圍 2
1.4研究方法 2
1.5研究流程 2
第二章 文獻回顧 4
2.1基因規劃法文獻回顧 4
2.2資料填補文獻回顧 7
2.3小結 10
第三章 研究方法 11
3.1基因規劃法 12
3.2績效評估 19
3.2.1 變異數分析 19
3.2.2平均數差異檢定 20
第四章 實驗設計 24
4.1單一屬性資料輸入填補設計 24
4.2多屬性資料輸入填補設計 26
第五章 實證分析 29
5.1 資料前置處理 30
5.2單一屬性資料輸入填補 31
5.2.1流量填補 33
5.2.2速率填補 35
5.2.3佔有率填補 37
5.2.4小結 39
5.3多屬性資料輸入填補 40
5.3.1流量填補 42
5.3.2速率填補 44
5.3.3佔有率填補 47
5.3.4小結 48
5.4 GP填補績效比較 49
5.4.1流量填補績效比較 49
5.4.2速率填補績效比較 53
5.4.3佔有率填補績效比較 57
5.4.4小結 61
5.5 GP與類神經填補績效比較 62
5.5.1流量填補績效比較 63
5.5.2速率填補績效比較 65
5.5.3佔有率填補績效比較 68
5.5.4小結 71
第六章 結論與建議 72
6.1結論 72
6.2建議 75
參考文獻 76
附錄A 80
附錄B 90
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指導教授 吳健生(Jiann-Sheng Wu) 審核日期 2011-12-20
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